Abstract
We study the scalability and efficiency of a GA that we developed earlier to solve the practical cartographic problem of labeling a map with point features. We argue that the special characteristics of our GA make that it fits in well with theoretical models predicting the optimal population size (the Gambler’s Ruin model) and the number of generations until convergence. We then verify these predictions experimentally. It turns out that our algorithm indeed performs according to the theory, leading to a scale-up for the total amount of computational effort that is linear in the problem size.
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van Dijk, S., Thierens, D., de Berg, M. (2000). Scalability and Efficiency of Genetic Algorithms for Geometrical Applications. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_67
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DOI: https://doi.org/10.1007/3-540-45356-3_67
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